Numerical investigations of escape panic of confined pedestrians have revealed interesting dynamical features such as pedestrian arch formation around an exit, disruptive interference, self-organized queuing, and scale-free behavior. However, these predictions have remained unverified because escape panic experiments with real systems are difficult to perform. For mice escaping out of a water pool, we found that for a critical sampling rate the escape behavior exhibits the predicted features even at short observation times. The mice escaped via an exit in bursts of different sizes that obey exponential and (truncated) power-law distributions depending on exit width. Oversampling or undersampling the mouse escape rate prevents the observation of the predicted features. Real systems are normally subject to unavoidable constraints arising from occupancy rate, pedestrian exhaustion, and nonrigidity of pedestrian bodies. The effect of these constraints on the dynamics of real escape panic is also studied.social behavior ͉ structures in complex systems ͉ dynamics of social systems S earches of disaster databases § would readily show that the escape panic of confined pedestrians is costly in terms of fatalities and property loss. Despite the huge toll inflicted by these incidents to society, the dynamics of escape panic are not completely understood because studies have been largely confined to numerical simulations that revealed a number of interesting dynamical features such as pedestrian arch formation around an exit, herding, and interference between arches in multiple-exit rooms (1). Recently, additional features such as disruptive interference, self-organized queuing, and scale-free escape dynamics (2) were found. Experiments in genuine escape panic are difficult, especially with humans because of possible ethical and even legal concerns.This work addresses the behavior of panicking groups and how it is influenced by the architecture of the space in which they are confined. It determines whether the dynamical features predicted earlier in numerical experiments are observed in a group of real biological (nonrigid) agents undergoing escape panic. Experimental results from mice escaping out of a water pool show that for a critical sampling interval their escape behavior agrees with the numerically predicted exponential and powerlaw frequency distributions of the exit burst size even for short time durations. Oversampling or undersampling of the mouse escape rate prevents the observation of the predicted features.Escape panic could happen in different confinement sizes, from a rioting crowd in a packed stadium to stunned customers in a smoke-filled bar. It is characterized by strong contact interactions between selfish individuals that quickly gives rise to herding, stampede, and clogging (3-7). Escape panic is simulated by solving a set of coupled differential equations (1,8) or by the cellular automata (CA) technique (2) where the movement of confined pedestrians is tracked over time. Both approaches yield consiste...

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Word adjacency networks constructed from written works reflect differences in the structure of prose and poetry. We present a method to disambiguate prose and poetry by analyzing network parameters of word adjacency networks, such as the clustering coefficient, average path length and average degree. We determine the relevant parameters for disambiguation using linear discriminant analysis (LDA) and the effect size criterion. The accuracy of the method is 74.9 ± 2.9% for the training set and 73.7 ± 6.4% for the test set which are greater than the acceptable classifier requirement of 67.3%. This approach is also useful in locating text boundaries within a single article which falls within a window size where the significant change in clustering coefficient is observed. Results indicate that an optimal window size of 75 words can detect the text boundaries.

The availability of accurate and reliable rainfall data that are applicable to various phenomenological, climatological, and modeling studies is important, especially in the Philippines, which is considered to be highly vulnerable to natural hazards and a changing climate. The presented strategy involved constructing a dataset consisting of synoptic data, automatic rain gauge (ARG) measurements, and satellite data that are co-registered, consistent, and formatted in the same manner. Although sparse in number, the synoptic stations provide the most accurate rainfall information and were used as the baseline for creating the dataset. The ARGs that are within a distance of 1 km to the synoptic stations were used to determine the correction factors needed to make the synoptic and ARG data consistent. Subsequently, the corrected ARGs were used to make the satellite IMERG data consistent with both ARG and synoptic data. In case of the latter, only IMERG pixels with at least 10 ARGs within the relatively large footprint of the satellite sensor were used in estimating the required correction parameters derived from a combination of a power transform and linear regression correction techniques. The final results show good agreement of synoptic and corrected ARG data with correlation coefficients of 0.94 and 0.97 for the 10 day and monthly data, respectively, and improvement in the linear regression slope from 0.67 to 0.90 for 10 day data, and 0.70 to 0.94 for monthly data. In addition, the corrected ARG data agree well with the corrected IMERG data, with correlation coefficients of 0.88 and 0.93 for the 10 day and monthly data, respectively, and an improvement in slope from 0.66 to 0.87 for 10 day data, and 0.74 to 0.99 for monthly data. The merit of using a combined dataset is illustrated through comparative analyses of the IMERG data and spatially interpolated synoptic and ARG data. The results show general agreements in spatial patterns of rainfall across the datasets, especially in areas where in situ measurements are recorded. The observed discrepancy when ground data is limited emphasizes the need for satellite IMERG data to obtain the true spatial patterns of rainfall distribution.

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